Determinants of Infant Mortality in Bangladesh: A Nationally Surveyed Data Analysis
Bibliographic record
Abstract
Background: It is well established that improving human health has direct obvious payoff on enhancing life expectancy along with economic growth. Infant mortality deliberately used to understand a countries overall public health status particularly child bearing mothers. But the prevalence of child mortality continues to be a prime public health concerns in Bangladesh. This study aims to investigate the impact of some geospatial, socioeconomic, demographic and health factors on infant mortality in Bangladesh. Methods: The study modeled infant mortality (aged 0-11 months) as the categorical dependent variable using 11 selected covariates from the 2014 Bangladesh Demographic and Health Survey (BDHS-2014) dataset. The Pearson-Chi square test and Binary Logistic Regression methods were utilized for the bivariate and multivariate analyses. Results: All the selected covariates were significantly associated with infant mortality in bivariate analysis. The results of the logistic regression revealed that illiterate father, household without toilet facility or having hanging toilet, multiple birth and small size at birth appeared at the significant risk factors for infant mortality. In contrast, receiving vitamin A dose and visiting in antenatal care revealed as protective factor for infant deaths. Conclusion: This study is uniquely addressed some several determinants which are the immediate cause of infant deaths. This evidence based empirical study suggests that more attention needs regarding to eliminate all kinds of child mortality in Bangladesh along with infant mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".